NDLI: Ensembles of classifiers and their aplication to bioinformatics problems

Content Provider	IEEE Xplore Digital Library
Author	Palade, V.
Copyright Year	2010
Description	Author affiliation: Oxford University Computing Laboratory, University of Oxford, Wolf son Building, Parks Road, Oxford, OX1 3QD, UK (Palade, V.)
Abstract	Machine Learning has become a very popular approach in addressing problems in the Computational Biology and Bioinformatics area. In addition, multi-classifier systems have also gained popularity among researchers working in machine learning and applications for their ability to fuse together multiple models and obtain better overall accuracy and classification results. This talk is concerned with current issues in the design of multi-classifier systems and presents some multi-classifier developments for several bioinformatics problems. The talk will first present an overview and current status of machine learning methods in bioinformatics and computational biology. The talk will then bring in some important issues in building ensembles of classifiers, with a focus on the diversity and combination of individual classifiers. Few diversification and combination schemes are presented along with guidelines for the selection of different training paradigms and performance metrics, based on the properties and distribution of the data. Then, the presentation will proceed with introducing our computational intelligence based multi-classifier developments for solving several bioinformatics problems, such as recognizing sequences in DNA strings, micro-array gene expression data analysis, protein structure prediction. The talk will also present related machine learning issues in developing such systems, such as learning from imbalanced datasets and using appropriate performance metrics for model selection and evaluation. The presented approaches and results will advocate that ensembles of classifiers can be used as effective modelling tools in solving challenging bioinformatics problems.
Starting Page	1
Ending Page	1
File Size	217216
Page Count	1
File Format	PDF
ISBN	9781424491674
ISSN	10637125
e-ISBN	9781424491681
DOI	10.1109/CBMS.2010.6042609
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-12
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bioinformatics Machine learning Biological system modeling Computational modeling Buildings Educational institutions Computational biology
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Computer Science Applications

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